Short-term weather predictions (e.g., 10–120 minutes) of the location and severity of storms are extremely important to many sectors of the population. For example, aviation systems, traffic information systems, power companies and commuters realize important safety and economic benefits from accurate predictions of storms. Short-term forecasts are particularly important for convective storms, such as thunderstorms, in which individual cells can exhibit a lifecycle less than the short-term forecast period. The challenge for any short-term forecaster is generating a forecast that is both accurate and reliable.
Some methods of generating short-term convective weather forecasts are partially automated, relying on operator invention. These approaches can offer acceptable predictabilities, however, can require significant operator interaction. As with any application relying on operator intervention, there is a possibility that human error can result in inaccurate forecasts.
Other methods of generating short-term convective weather forecast require little or no operator intervention. Unfortunately, the accuracy and reliability of these systems is generally insufficient for many applications. Fully-automated systems often “over predict” severe weather events. Such forecasts can exaggerate storm intensity and spatial extent. For applications, such as air traffic control, an over prediction can result in rerouting air traffic unnecessarily, resulting in undesirable inefficiencies, including longer flight times and additional fuel consumption.